Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle

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• 作者：Joonho Lee ; Hao Cheng ; Dorian Garrick ; Bruce Golden…
• 刊名：Genetics Selection Evolution
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：49
• 期：1
• 全文大小：1382KB
• 刊物主题：Animal Genetics and Genomics; Evolutionary Biology; Agriculture;
• 出版者：BioMed Central
• ISSN：1297-9686
• 卷排序：49

文摘

BackgroundGenomic predictions from BayesA and BayesB use training data that include animals with both phenotypes and genotypes. Single-step methodologies allow additional information from non-genotyped relatives to be included in the analysis. The single-step genomic best linear unbiased prediction (SSGBLUP) method uses a relationship matrix computed from marker and pedigree information, in which missing genotypes are imputed implicitly. Single-step Bayesian regression (SSBR) extends SSGBLUP to BayesB-like models using explicitly imputed genotypes for non-genotyped individuals.